Quality of life during the epidemic of COVID-19 and its associated factors among enterprise workers in East China
Bibliographic record
Abstract
BACKGROUND: The COVID-19 related lockdown and home confinement might have an important impact on the quality of life in enterprise workers. We investigated the quality of life during the epidemic in enterprise workers who just returned to work, and assessed its potential influencing factors to have a better understanding of the impact of COVID-19 epidemic lockdown and home confinement. METHODS: This was a cross-sectional study of enterprise workers conducted in Deqing and Taizhou, Zhejiang Province, China. The Chinese version of EQ5D was used to assess life quality, and information about general characteristics and COVID-19 related factors was collected by a structured questionnaire, which was distributed through the social application "WeChat". Multiple liner regression was used to investigate potential influencing factors. RESULTS: A total of 2420 participants were enrolled, 59.5% of which worked in Deqing. About 50% of the participants reported worries about the COVID-2019 epidemic and 40.2% had a centralized or home quarantine during the epidemic. The mean EQ-5D score and VAS were 0.990 and 93.5. Multiple liner regression showed that the quality of life measures was related to physical activities (β = 0.006) and keeping home ventilation (β = 0.063) in Deqing, and were related to wearing a mask when going out (β = 0.014), keeping home ventilation (β = 0.061), other marital status (β = - 0.011), worry about the epidemic (β = - 0.005) and having a centralized or home quarantine (β = - 0.005) in Taizhou. CONCLUSIONS: The quality of life for returning enterprise workers in areas with different risks of COVID-19 was affected by different factors. Associated factors identified from this study would help develop proper intervention measures for enterprise workers to reduce the impact of large-scale public health events like the COVID-19 on their quality of life.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".